For example, in finding target names of prominent Australians in the data we obviously see many instances of different individuals with the same name.

alveo also aims to provide access to the data in a way that facilitates automatic processing of the text rather than the document-by-document interface provided by the Trove web aPI.


The corpus contains a wide range of document types from the wide range of newspapers included in the Trove archive.

doing any computational work on the entire Trove corpus or even on significant subsets is not efficient using the Alveo (or Trove) web APIs.


The term less commonly taught seems to have been borrowed in the HLT community from the second language teaching community where it refers to instruction within a specific target market, be it the United States, the Western Hemisphere or perhaps outside of the region where the language is official.

notwithstanding the number of native speakers, if most of those also speak another language with an even greater population or prominence, that could reduce the language's importance to projects whose goal is to develop news understanding technologies.


Data are typically segments of broadcast and telephone conversations audited for the linguistic variety spoken, speaker number and sex, and sound quality.

after some turn-over in project management, TIDES focused the bulk of its attention on English, (Mandarin) Chinese and (Modern Standard) arabic but planned for Surprise Language Exercises.


Similarly the nature of the morphology affects HLT development, not only whether the language tends toward an analytic or synthetic morphology but also such factors as the number of morphological classes and the degree of irregularity and syncretism present.

differences in the terminology, available information and goals of low resource language efforts lead to variability and some obscurity in the language selection process.


These textual productions are updated by new entries more often than static content.

the non-controlled use of terms, the absence of (standard) orthographic conventions, loose grammatical constraints, and high dependence on the individuals' writing style, give rise to data sparsity issues affecting the performance of SMt systems, which present a poorer performance with informal genres (van der Wees et al., 2015a).


APSyn was tested on the ESL and TOEFL questions, outperforming the Vector Cosine and the co-occurrence, plus several lexicon-based and hybrid models.

the former rely on lexicons or semantic networks, such as WordNet (Fellbaum, 1998), measuring the distance between the nodes in the network.


83.33% = 0.833) and considered for our task only sentences for which the difference between L1 and L2 was less than 0.30 5 .

independently of the different conceptual framework adopted, the majority of the systems in literature model metaphor as a discrete property, ignoring the fact that several degrees of metaphoricity are possible.


In order to include only prototypical pairs in EVALution-MAN, we filtered the CWN pairs and then further assessed them through manual annotation through both rating and tagging methods.

the relatas'frequency was calculated in a combined corpus of Sinica and Chinese Gigaword.


zalupati 'start banging' to lupiti 'hit') • We separate suppletive forms and do not explicitly relate them to each other (e.g.

the two verbs are morphologically related.


VoxML treats objects and events in terms of a dynamic event semantics, Dynamic Interval Temporal Logic (DITL) (Pustejovsky and Moszkowicz, 2011).

when we impose more constraints on the values of an attribute, we arrive at more structured domains.


To evaluate this approach, test sentences used in the previous study were prepared and the results from the proposed method were compared with those of the previous study.

in the 90 test sentences, there were 108 nouns the proposed method and baselines determined as 'Metonymic' or 'Literal'.


In other words, each is an accuracy of the first correct word in the top N. As shown on the table, the proposed method expressed higher accuracies than the baselines.

table 9 lists two rates of correctness for each metonymic concept.


It uses synsets to obtain node distance and the rest to do the same as CF-GT.

the proposed method exhibited higher accuracy (0.85) than the two baselines.


We evaluated the quality of the common-sense knowledge obtained by the framework presented in this paper through manual review of randomly sampled relations encoded in the knowledge graph, as well as implicit relations inferred in the embedding space.

perhaps the most popular of them is the Unified Medical Language System (UMLS) (Bodenreider, 2004) which designed by the National Library of Medicine in order to provide a standard set of concepts allowing for commonalities amongst various medical terminologies to be interlinked, for example, by mapping ICD-9 diagnostic codes (Cimino et al., 1993) to Medical Subject Headings (MeSH) (Lowe and Barnett, 1994).


The reports include the patient's clinical history and medications, a description of the EEG setting and configuration, as well as the physician's impressions and clinical findings.

similarly, medical knowledge is learned from two large sets of electronic health records.


Because we considered lexical relations between concepts, rather than individual words, the resulting knowledge graph was quite large, containing 32,522,807 vertices and 52,209,411 edges.

this demonstrates that the embedding procedure is able to account for the affects of individual words on the semantics of the discovered lexical relationships.


Attempts to address these limitations was tackled in our previous work (Goodwin and Harabagiu, 2013) where a qualified medical knowledge graph (QMKG) was presented.

h(•), uses the bag-of-words vector representations expressing the lexicalized relation to generate a vector representation of the relation.


We preserve the structures of infoboxes and tables in our extraction.

as the pages of the websites we used for the knowledge database are divided into sections, we believe that this problem can be at least partially solved by using the names of these sections to solve some further co-references and lacks of context.


We use a Davidsonian style representation for events; for example, an entity x 1 dropping an item x 2 is represented as drop(e), dropper(e, x 1 ), dropped(e, x 2 ).

our goal is to build a QA system that can translate a question asked by Non-factoid questions: What is the best way to spawn the two different types of Golem?


Coverage is already very high with the first model but deviations from annotated phone boundaries in terms of RMSE continue to decrease with iterations.

we most certainly not yet maxed out the alignment coverage.


Table 6 shows accuracies of ASR with models adapted by the adaptation technique proposed in our previous work (Hirayama et al., 2015).

we evaluated the collected data in automatic speech recognition (ASR) and Kana Kanji conversion (KKC) (Mori et al., 1999;Takahashi and Mori, 2015) systems.


Tireless efforts of data collection of speech data and their transcriptions from the early stage of NLP and SLP researches drastically improved accuracies of a variety of NLP and SLP tasks.

dialect speakers convert the selected 100 sentences into their own dialect sentences.


The originality of the corpus is to allow a comparison between controlled and less controlled speech in the two populations.

in each class, phones are characterized by a set of features considered as relevant for the discrimination task (see Laaridh et al.


This new speech resource will further support the development of speech and language technologies for under-resourced language.

they were reviewed in order to best meet the needs of ASR.


In recent years, large databases of spoken Polish have been collected such as Jurisdict (Demenko et al., 2008), Diagest (Jarmołowicz et al., 2007) or Paralingua .

for the needs of studying cross-linguistic perception and production of prominence and phrasing, stability of rhythm metrics and rhythmic classification of Polish, we also recorded non-native speakers in Polish and in their L1s (see: Sec.


Our other serious interest is focused on the DNN-based LVCSR for Slovak using Kaldi (Povey et al., 2011).

we report here the preliminary results (see Table 2.2.)


The generated hypotheses are then manually corrected and completed by trained human annotators.

the new acoustic data will be appended again to the already expanded training data and the process of LVCSR system retraining and semi-automatic annotation will iteratively proceed until a sufficient amount is reached (usually hundreds of hours).


2 hours of spoken language recordings with transcription, and a set of basic language models like a pronunciation dictionary and a description of the phone inventory of the language.

additionally, the project adheres to and promotes bestpractice standards in the development of language data and tools.


For the same reason, books are not read entirely.

we chose two chapters that were near the mean of overall alignment scores (hypothesized medium quality alignments), one chapter which was above the mean score (hypothesized good quality alignment) and a final chapter below mean score (hypothesized bad quality alignment).


However, these metrics only provide global evaluation and are unable to evaluate MT systems' performance on specific aspects.

we further select only idioms whose translation is non-compositional, and create a blacklist for each idiom.


One of the most popular semantic resources for English is WordNet (Fellbaum, 1998;Miller, 1995).

due to these problems, although we consider using WordNet in the future both as a resource and as a possible benchmark, we did not use it in the experiments presented in this paper.


In practice, we repeat this process 5 − 6 times to build tokens representing longer ngrams.

we used English Wikipedia from June 2017, from which we used the meta pages archive which resulted in a text corpus with more than 9 billion words 3 .


For example, one of the intransitive frames in the class Run-51.3.2 is shown in (1), with the final 4 lines making up the semantic representation: (1) The horse ran into the barn.

a similar sentence with an agent causing the motion, such as John herded the sheep into the barn, would add cause(agent, E) to the semantic representation in (1).


This paper focuses on resource building, and the corpus of old Vedic is, more or less, restricted to the R .

the system has a remarkably high tokenization accuracy.


In this task, we corrected the errors in the processes, and performed the POS tagging and morphological analysis manually for 450 cases.

if a given target word in a sentence is disambiguated as a specific frame-semantics by the target identification task, its frame elements are identified if only it matched with the valence patterns.


Finally, we also input as onehot vectors external lexicon information, constructed using the Lefff lexicon (Sagot, 2010).

these word embeddings can be initialized with pretrained embeddings and/or learned when training the model.


As expected, the proposed approach is efficient for diacritics, apostrophes and 1 letter modifications (DIACR, APOST, MOD1C).

it is worth noticing that in our approach, as the lexicon used in the correction step is not exhaustive, we observe 80 added errors due to the fact that some words, which were correct in the raw text, but not present in the lexicon, have been erroneously modified into an in-vocabulary form.


On the output layer, we use a softmax activation.

it is inefficient for agglutination AGGLU and SPLIT, and word error rates (lines four to six).


We investigated two variations of this model: (i) a bidirectional LSTM model with sequential CRF layer on top (LSTM-CRF) treating the problem as a discrete classification task, (ii) a new bidirectional LSTM model with a sigmoid layer on top (LSTM-SIG) for a continuous prediction.

while the LSTM-CRF had a better (higher) F-score on the classification task, its RMS score was worse (higher) than the LSTM-SIG model, which may be due to the limitation of the model as discussed in Section 5.


in our research for DHH users, we have found that an evaluation metric designed for predicting the usability of an ASR-generated transcription as a caption text for these users could benefit from word importance information (Kafle and Huenerfauth, 2017).

the LStM layer size was set to 300 in each direction for word-and 100 for character-level components.


Taxi C, D and A would have force themselves through far more road construction works and traffic jams than taxi B.

in our experiment, we assessed trust, reliability, predictability, competence, acceptance and likability of the robot as dependent variables.


The same procedure was repeated for the second task scenario.

we tested each gender-manipulated variable on perceived masculinity and femininity by the user.


Given the variety of available objects they would need to compromise their choices and collectively decide where to place objects.

no significant difference was found on the same case for the listeners' gaze [t = -0.767, p = 0.444].


Then, the dialogue act of current utterance can be formally defined as: ( ) denotes the dialogue act of the i th utterance ( ) and , denotes jth candidate dialogue act of the ith utterance ( ), given a dialogue including n utterances ( 1, ).

(Bahou et al., 2008) used utterances semantic labeling based on the frame grammar formalism.


This is particularly important to ensure that the principle of data minimization (see above) is respected.

it is necessary to periodically review the results of anonymization processes.


The portal gives a web-based access, and for this it makes use of a database that holds information about access rights to digital objects, and information about users and their authorization records.

the aim was to explore how to best transfer the management of research data to these units for the longtime archiving of linguistic resources.


The data centre cooperates closely with the CLARIN-D centre in Tübingen which is funded by the German Federal Ministry of Education and Research (BMBF).

there are differences in the use of ACLs, and where they are stored.


We envision any process used to collect linguistic data as comprised of the interaction of several components: task, incentives, workforce, workflow and processing.

linguistic data is presented, selected and segmented, segments are classified and labels, annotation records are stored along with information concerning the source media, time and task and 4 https://lingoboingo.org contributors.


A key feature of Wiki sites is to allow people from all over the world to add or modify articles anonymously and without consequence.

if high precision values are more desirable, then one-class classification should be used instead.


It was created using best-worst scaling in a similar approach described earlier in this paper for obtaining emotion intensity scores.

we refer to the full list of words along with their real-valued scores of affect intensity as the NRC Affect Intensity Lexicon.


Antonymous terms tend to co-occur with each other more often than chance, and are particularly problematic when one uses automatic co-occurrence-based statistical methods to capture word-emotion connotations.

a notable exception to this is the NRC Emotion Lexicon, which includes words that are associated with (or connotate) an emotion (Mohammad and Turney, 2013).


Overall, we observe that the three negative emotions can be conveyed by words having a wide range of values for valence, arousal, and dominance.

sadness words tend to have slightly lower dominance scores than fear and anger words.


We find that anger, fear, and sadness words, on average, have very similar VAD scores.

for each of the eight emotions, we created separate lists of terms that satisfied either one of the two properties listed below: • The word is already known to be associated with the emotion (although the intensity of emotion it conveys is unknown).


For comparison against the human ceiling, we found eight pairs of emotion lexicons with partially overlapping entries distributed over four languages.

unsurprisingly, Valence displays a strong positive correlation with Joy and strong negative correlations with the remaining BE5 categories.


The last dimension, Dominance, is sometimes omitted, leading to the VA model.

the lexicons as well as the source code for building them are publicly available (see Section 6.).


This outcome led us to conclude that the relationship between VAD and BE5 ratings is not fully constant across different languages (respectively data sets).

this stream of work does not target the goal of predictive modeling (Stevenson et al., 2007;Pinheiro et al., 2017).


Stress results in increased sympathetic activity and can be tracked for example through bodily reactions, such as an increase in heart rate, greater blood flow to extremities and an increase in the respiration rate etc.

a dyadic team corpus was used to examine the association between the "defusers" physiological signals following the "instructor's" questions to predict whether they occurred in a short or long turn-taking period of time.


The notch of a box shows the confidence interval which is normally based on the median±1.15 * /√ (the interquartile range (IQR) is the 25 to 75 percentage).

recall will be significantly underestimated.


We have compiled in a unsupervised way a large resource of pattern paraphrases that is available for academic purposes.

we can now estimate how many pairs were correct on average for each approach, and how many correct paraphrases were contributed to the pool of correct paraphrases by each approach.


For this sentences we can compare the accuracy of the whole text, not only of the verb.

this is a direction that we would like to exploit further.


Based on this observation, we developed a simple rule-based tokeniser in which 's was separated from the remaining part of the token if it was not a noun.

as expected, tagging accuracy (F1 score) was lower if the training data and test data were derived from different domains.


DS errors are perhaps the hardest to agree on as each gold standard could have different annotation guidelines.

the Fleiss's κ agreement figures show that there is a high agreement between the human annotators (a value between 0.61 and 0.80 denotes substantial agreement, while a value between 0.81 and 1.00 denotes an almost perfect agreement).


We selected 200 abstracts for annotation from PubMed, according to the criteria that each abstract should contain at least one of the relation types that we were looking for.

table 3: the averaged statistics per document for both abstracts and full papers in our corpus.


The biomedical track at WMT 2017 (Jimeno Yepes et al., 2017) continued to offer new resources for French (EDP corpus), Portuguese and Spanish (extension of the Scielo corpus).


In this paper, we present the process of creating an annotated, phrase-level PICO (Population, Intervention, Comparison, Outcome) corpus and a sentence-level sentiment analysis corpus.

meanwhile, the non-experts had difficulties in identifying Populations that had no reference to a Person entity (e.g.


Since there was no appropriate data available to create an automatic PICO approach or sentiment classifier, we created it ourselves with the help of expert and nonexpert annotators.

an Intervention that did not perform better than its Comparison, should be classified as negative.


If to do so, Hazem and Daille (2014) use distributional approach as introduced in Subsection 2.2., here we use distributed models (Mikolov et al., 2013b).

given the multi-word candidate terms CCT 1 = (T 1 , E 1 ) and CCT 2 = (T 2 , E 2 ) and syn(CT 1 , CT 2 ) a synonym relation between the candidate terms CT 1 and CT 2 , the following inference rules are used: In rule R 1 , the heads are identical and the expansions are synonymous, while in rule R 2 heads are synonymous and expansions are identical.


This work was supported by the EC's FP7 (FP7/2007(FP7/ -2013 under grant agreement number 610516: "QTLeap: Quality Translation by Deep Language Engineering Approaches".

for example, with θ ≤ 0.1 we restrict the MWE candidate list to contain only those items whose compositionality score is less than or equal to 0.1.


The entries of these types of lexicons are mostly composed of MultiWord Expressions (MWEs).

the result of the previous step (Extraction and alignment of MWEs) is a list of alignment links candidates.


In this example, the bilingual dictionary provides the alignments: "be/être", "decided/décidé" and "there/y".

to solve this program, we used two scoring functions.


This approach is hybrid because it considers the global task of identification and alignment of MWEs as an optimization problem and it uses external linguistic resources: a seed single word bilingual dictionary and morpho-syntactic patterns.

to traditional approaches for MWEs alignment which consist in firstly identifying monolingual MWEs candidates and secondly applying alignment to find bilingual correspondences, our approach extracts and aligns MWEs in a one-step process.


One of the points is to annotate not only expressions with strong temporality but also expressions with weak temporality, such as states and habits.

the major cause of the disagreements among the annotators is that there are multiple interpretations depending on the context and common sense, closely related to the writing style and theme of newspaper.


The same applies to the e-e pairs, where out of 54 system output links, 44 are valid with 26 (59.09%) correctly classified.

solving this cases would require a system to apply inference mechanisms.


The techniques could be integrated in for instance a reading aid, an activity mainly focused on receptive understanding.

the corpus is then used to retrieve paradigmatically analogous constructs, resolving the abstract slot, as seen in table 1.


A total of 22 subjects participated to the recordings of the speech data: one native speaker for each language, and 10 learners for each language.

corpus-based studies can be used to determine how some morpho-syntactic phenomena are acquired in English as a foreign language (see for example the project English Profile, Hawkins and Buttery 2009), how students' pronunciation of L2 can be influenced by his L1 (I-PFc project, Racine et al.


This kind of questionnaire presents the advantage to force the subjects to perform certain utterances for a specific given context.

the prosodic tier is completed manually, and the misc tier serves for remarks.


In the last years, especially with the development of new methodologies and new technologies in corpus and computational linguistics, language corpora have become more and more common and needed in linguistic research.

it is a rather difficult task for beginners or intermediate learners.


The FLAIR text classification is based on parsing information and on the official English language curriculum of schools in Baden-Württemberg .

indeed, the sentence level alignment, also presented by Kauchak (2013), was possible in only 28% of the sentences from the SEW (and in 4.25% of the sentences from EW).


This is similar to SourceFinder's method, in which on-line newspapers and magazines are downloaded and processed as text sources (Passonneau et al., 2002;Sheehan et al., 2007).

in spite of all the effort to present readable and interesting texts to learners, those systems do not indicate how learners can improve their skills by using the indicated texts.


in a case like http://www.ims.uni-stuttgart.de/ institut/mitarbeiter/roesigia/ guidelines-bridging-en.pdf where some people might consider this a bridging case, as the foreign secretary Mottaki is probably not interpretable alone for a typical WSJ reader without the mentioning of Iran first.

the annotation is done using the annotation tool Slate (Kaplan et al., 2012) 4 .


For the single anaphor type categories (definite, indefinite, comparative) we have created separate columns, as well as one joint column which contains all the bridging links.

only a small subset of the annotated pairs contains truly anaphoric bridging anaphors, which is why annotated corpus resources like the one presented in our paper are still beneficial (c.f.


Dominance of negative examples over positive is a typical situation in real texts, where most pairs are not coreferent.

each experiment (excluding experiment 3) was tested on a set of various different pre-selected connection threshold values: 0.5, 0.75, 0.85, 0.95, and 0.99.


The data for our experiments, as for all previous configurations, come from the Polish Coreference Corpus (Ogrodniczuk, 2015, PCC), a large corpus of Polish general nominal coreference manually annotated over the texts of the National Corpus of Polish (Przepiórkowski et al., 2012) 1 and Rzeczpospolita Corpus (Presspublica, 2002).

the network takes an input vector of 1147 units and is passed through a fully connected network with a single output (a value between 0 and 1).


figure 2 tells us that repetitions, hypernyms and adverbial anaphors are much more frequent in the student essays than in the newspaper articles.


Results from SANAPHOR have not been reported because it has been built over the Stanford decoref that uses the CoNLL specific features and then cannot be compared with the actual models of Sanaphor++ and Stanford deep-coref.

it means that if an antecedent is not an empty antecedent and if the set of true antecedents is equal to empty, then this antecedent is likely to be a wrong pair;  belonging to the hierarchy of type of the antecedent is in the set of the true types, then this antecedent is likely to be a good pair.


We have presented a coreference resolution system that is able to capture the semantic of the entities into a single method based on two different systems that use divergent methods.

we decided to create a new threedimensional vector, one dimension for each semantic feature: i) if the entities of the two mentions are the same, ii) if the type of the two mentions are the same, and iii) if the type of one mention is included into the hierarchy of the other one.


This version adds syntactic annotations in addition to the existing coreference and speech transcription ones.

2016) through state-of-the-art automatic parsing techniques.


The only resources for the language pair English-German that are known to us include the GECCo corpus (Lapshinova-Koltunski and Kunz, 2014), the ParCor corpus  and the multilingual coreference corpus described by (Grishina and Stede, 2015).

moreover, negation in German can be expressed not only with the adverb nicht, but also with the indefinite pronoun kein.


The coreference relation is shared across all languages.

the annotation scheme takes inspiration from the schemes used in all the three resources mentioned above (Kunz, 2012;Grishina and Stede, 2016).


For studies on Japanese dialect, (Yoshino et al., 2016) collected a parallel database of Japanese dialects with voice recording in their well-equipped recording environment.

1 https://github.com/mattdiamond/ Recorderjs Table 1: List of collected dialects.


Vocabulary knowledge prediction is an important task in lexical text simplification for foreign language learners (L2 learners).

in this section, we describe the content and format of this resource.


Training on larger annotated corpora showed only very small increases in performance.

previously studied methods that use hand-crafted rules based on one or two word features have had limited success.


In case several transcripts have been proposed for a title area, they are all proposed in order to associate the best one or to correct the closest.

this is illustrated in Figure 1 by the red dotted arrow.


By studying the theater that was excluded from the system of privileges, the Italian Theater, we analyze the questions of acculturation and institutionalization including the fusion of the Opera-Comique with the Italian Comedy.

the diversity and complexity of data contained in a daily page of Italian comedy require types and tags to be more specific and hold-back all levels of annotation.


Crowd workers were instructed to inspect each image and then produce a plain text description of the fashion item.

recent years have seen a renewed interest in text-image multi-modality and have seen the emergence of tasks such as automatically generating textual captions for a given image (Karpathy and Fei-Fei, 2015;Lu et al., 2016), using plain text descriptions to query images (Socher et al., 2014), and matching lexical tokens or constituents to regions in an image (Ma et al., 2015).


In German, a simplification corpus with a scale of 7,000 sentences (Caseli et al., 2009), In Italian the PaCCSS-IT (Brunato et al., 2016) with a scale of 63,000 sentences, In Spanish a simplification corpus do exist made by hand by the few rules (Mitkov andŠtajner, 2014;Stajner et al., 2015), and so on.

red, green, and blue were necessary to explain the concept of color.)


The resulting data set will be made available through the EU (according to the H2020 Open Research Data Pilot) for research purposes after the end of the project, excluding the sentences sampled from Coursera as these were restricted by copyright to project-internal usage only.

(1) Agrippa 's Trilemma states that there are three options if we try to prove the truth .


As Jiménez-Crespo (2017) observes, Facebook has used it for the translation of its social networking site and its user interfaces, Amara and TED for audiovisual practices, Kiva and the platform Trommons from the Rosetta Foundation for non-profit initiatives.

the subtitle genre contained spontaneous speech properties, truncated sentences, elliptical formations, disfluencies, repetitions, interjections and fillers.


Presumably, due to the small training dataset, the SVR with an RBF kernel was overfitted, and its RMSE scores were worse than those in the case when linear kernels were used.

item response theory is used like a name of a field rather than a specific models.


This is probably because 1) most of the tests are used to grade test takers, i.e., placement tests; thus, they are treated as private information that should not be leaked, and 2) the primary focus of most language-educators is how to measure their students' ESL vocabulary, rather than the test results of the other test takers.

we did not include English proficiency tests popular almost solely in Japan, such as the "Eiken" test, an English proficiency test popular with Japanese high-school students while we required our test takers to have a TOEIC test score, it did not matter when they took the test.


Let the set of test takers (learners) be I and the set of problems be J.

this is not the case for developing educational software, in which participants are more diverse than in typical classrooms.


Other interesting work such as (Chen et al., 2012) introduces a Deep Belief Network model that can handle highdimensional feature space (Lin et al., 2010) worked on a mixed model that combined feature-based and kernel-based models together.

besides, our model is not very language-sensitive, so we will extend our work to other languages.


Question is the uncertainty category with the most microblogs, followed by Possible.

), "如果" (if), and we also list the specific statistics of the agreement in each sub-class, as shown in Table 2.


In this case, the uncertain semantics will be implicitly conveyed by the whole sentence rather than by the explicit cue phrases.

we found that in Sina microblogs, there were a lot of sentences expressing the uncertainty statement with expected semantics.


Our future tasks include running a pilot survey to validate usability of survey format for our task and evaluate the adequacy and clarity of the questions for annotators.

merriam Webster dictionary defines spin doctor as "a person (such as a political aide) responsible for ensuring that others interpret an event from a particular point of view" 1 .


The goal of those previous location estimation researches is to estimate the locations vaguely with best-effort approaches.

we observed the variation of accuracy across the diseases.


Does not include "BOT", "Bot", "bot" in one or more of Twitter client name, account name, display name, and profile (1) of the above conditions is aimed at easily judging whether or not the result of estimating the position information from the tweet sentence is correct.

people have limited kinds of sources of information on diseases such as TV news and newspapers (including Internet news.)


Overall, the corpus contains 192 documents with 1,140 annotated temporal expressions, and has been made publicly available to further boost research in temporal tagging.

this work has been partially funded by the EUCLIP RES project under the FESR Program of the Autonomous Province of Bolzano -South tyrol.


Bethard, S., Savova, G., Chen, W.-T., Derczynski, L., Pustejovsky, J., and Verhagen, M. (2016).

the articles contain words and phrases which are not used in High German, including the temporal expression heuer which translates to "this year".


The workshop will constitute a forum for sharing a common wordnet structure across languages».

this study cannot be considered an exhaustive review of the field seen through the lenses of the LREC workshops because for the time being just a few features have been taken into account.


More thorough analyses can be carried out both on the research side for detecting the thematic trends and on the "societal" side for better defining this heterogeneous community constituted by the scholars who participated to the numerous LREC workshops.

guidelines and principles for comparing existing and acquiring new languages are of utmost importance in the field.


Manually annotated corpora are one of the pillars of research in NLP.

files (or URL strings) can be added via drag and drop functionality and immediately enriched with metadata.


Off-the-shelf database support for CoNLL TSV is currently limited to dialects with constant number of columns.

while, after conversion from CoNLL TSV, dependency labels are stored as literal values of conll:EDGE, it is easy to transform them into object properties using a simple SPARQL Update statement: As the example also illustrates, RDF resources can be defined in a way that their IRIs/URIs resolve against external resources, e.g., the dependency label nsubj yields the URL http://universaldependencies.org/ u/dep/nsubj and thus a human-readable definition.


CoNLL Merge and CoNLL-RDF are designed to be applicable to every OWPL corpus format and thus depend on user input regarding the labels and types of columns.

we provide means for (i) rule-based rewrite operations, (ii) visualization and manual annotation, (iii) merging CoNLL files, and (iv) data base support.


A loop -when an edge returns to the same token it started from -, is not part of the dependency formalism.

the thorough analysis of the errors can lead to new ideas and methods, and finally, to better results.


In this category we identify errors due to: i) the slightly difference between some frame meaning (e.g., many errors are due to the prediction of the frame Measure duration instead of Calendric unit); ii) the frequent absence of related senses so that is not possible to trigger any frame, even for correct BabelFy annotations.

we apply the methodology from Biemann et al.


In Figure 2 we draw the distribution of the counts of synsets across the number of frames that are triggered in the phase of candidate selection (step 1 of our WFD approach), i.e., in the XWFN profile, we counted ten thousand synsets bs for which |CF (bs)| = 1.

in the resource, each sense (e.g., read#VB#1) is defined by its related senses (quote#VB#0, recite#VB#0, and so on) which have been observed to co-occur with the sense.


For the bag-of-words features, we experimented with different window sizes but found that using all words in a sentence provides best performance.

since we can consider our task as some type of text classification task, we should examine simple surface-based features, such as bag of words.


(Sentences contain considerable fewer words than larger units of texts which are usually considered for text classification, such as paragraphs or documents.)

a more recent alternative to Brown clustering is the usage of word embeddings.


Further, we replace the hard mutual exclusive constraint from McIntosh and Curran (2008), which does not allow term and patterns to belong to multiple categories, with the soft constraint captured in Eq.

intuitively, we aim for a relatively high value for λ 1 because we prefer fewer, high-quality expanded terms (i.e., the expansion should be cautious).


Each premise provides a support in the form of logical reasoning for, or evidence in support of, the conclusion to which it is connected.

• Section 5. describes the SSS dataset that we create using opinions extracted a set of hotel reviews and SER.


This field of study has recently attracted a lot of attention due to its implications for businesses and governments.

sentiment analysis for English using the sanders dataset has been reported in a number of papers.


Using our system we achieved a significantly higher accuracy score of 78.3%.

this could be connected to the observation that the AStD dataset has the largest number of classes (5 classes compared to 4 in Sanders and 3 in Deutsche Bahn) and there could be an inverse correlation between the increased number of classes and the system performance.


We evaluate our system on three datasets in three different languages, and we find that stateof-art results can be achieved without language-specific features or pre-trained word embeddings.

we recommend data annotation to selectively target more data to increase the size of the minority classes to allows the system to better understand and predict these classes.


Of interest is also the quality of the text in the corpus and its applicability to other NLP tasks.

to investigate the noisiness of using Reddit as a source of self-annotated sarcasm we estimate the proportion of false positives and false negatives induced by our filtering.


While having only posts with at least one sarcastic response is useful, it also increases the false negative rate as comments warranting a sarcastic response often draw other sarcastic statements that are similar in content to the labeled sarcastic responses, but which themselves may not be labeled.

we evaluate the baseline performance of simple machine learning methods and compare them with human performance.


For the second case, we only keep comments which have the "/s" at the end of the comment.

a subreddit focusing on the discussion of web programming, for example, might include instances where "/s" is used with a different meaning.


Figure 3 shows the age distribution on the collected survey.

it is the first Brazilian Portuguese corpus about blogs.


Valuable additional resources are a lexicon with word class information and Brown clusters extracted from a web corpus.

horsmann and Zesch (2016) use the FlexTag tagger (Zesch and horsmann, 2016) with a conditional random fields (CRF) classifier.


We summarize the model architecture and its motivation here.

very little codeswitching corpora exist from which researchers can train statistical models.


On most of the other categories the system performs quite well.

the Foreebank (Kaljahi et al., 2015) has a very low percentage of normalized words.


When looking at Twitter public profiles, we found that some users may include information such as their real name, location and a short biography in their profile.

we also present some issues that we encountered during these phases.


All these features can be useful for learner corpora transcription.

all these features can be useful for learner corpora transcription.


It includes an annotation functionality serving historical research needs that can be transformed into a linguistic annotation functionality.

as the final users of the digital texts produced via the above mentioned crowdsourcing tools are historians, the annotation systems of these tools are adapted to bibliographical, paleontological and historical needs.


Naturally, averaging over loss scenarios with partly heavy loss will thus deteriorate average values but may be more faithful to historical circumstances.

alternatively, well-known clustering algorithms performed the cluster steps.


Starting from a number of prealigned observed variant texts, those are transformed, a) into pseudo-DNA and b) into bitvectors of so-called leitfehler.

the traditional scholarly concept of Leitfehler is taken to be a quantitative one: a variant's usefulness as Leitfehler may be assigned a number or weight.


The term autograph refers to (any) manuscripts written by an author him or herself.

this is rather secondary to the main aim of this paper, which is to present a method, which produces both stemma and archetype in conjunction.


Those graphical ambiguity and incompleteness would be solved by taking into account linguistic context such as proposed in Dhóndt et al.

in this work, we limited to most oldest (by Ur iii, about 2,000 BC) ones.


Since the oldest glyphs have complex shapes on each character class, we can distinguish classes more easily compared to newer glyphs as shown in Figure 2.

most of the candidates have been collected from the former, and supplementary employ the latter when we cannot find characters which belong to the target classes.


For example, there are about 70,000 digitally untranscribed hand copies out of all approximately 300,000 documents registered in CDLI.

few studies tried to detection of a character class from handwritten copies of original tablets (Massa et al., 2016;Rothacker et al., 2015), grammatical analysis (Homburg and Chiarcos, 2016) and automatic machine translation (Pagé-Perron et al., 2017).


To tackle to those touched and "complex" characters, it may be effective to apply some existing character recognition methods such as Rothacker et al.

most of them are ligatures of basic characters or less frequently appeared.


In the first stage, we create a 2D array where non-zero values represent the bounding boxes of text tokens in the PDF, then analyze this array for blocks.

it is important to maintain the visual alignment of text elements when projecting coordinate-positioned text in a variable-width font to columns in a monospaced plain-text file.


As the participants are being recorded on both video and audio this gives rise to both ethical, privacy and legal concerns.

one limitation is that the approach is restricted to dyadic corpus creation.


Many studies in particular have focused on the timing of feedback behaviours (Morency et al., 2010;Ward and Tsukahara, 2000;Cathcart et al., 2003).

crowdsourcing has been extensively used for annotations as well as transcriptions (Gruenstein et al., 2009;Hipp et al., 2013;Rashtchian et al., 2010).


Moreover, with respect to patient safety, appropriate strategies need to be defined in order to maintain full control of the medical devices even if IDACO is allowed to perform some pre-defined actions during the surgery and control devices automatically.

being hands-and eyes-free, speech as an input and output modality seems to be a good choice.


The only issue which remains open to debate is how the system should give feedback to the surgery team as, on the one hand, the surgeon should not be annoyed by unnecessary system prompts, but on the other hand, completely passive system behaviour makes the surgery team insecure.

the variety of information the user can request from the system in this mixed-initiative dialogue part is illustrated in Figure 1.


For the presented system, we modelled exemplarily the procedure of a laparoscopic cholecystectomy.

the complexity of the dialogue increases with the complexity of the surgery structure.


In this context, the Operating Room of the Future is a keyword often used (Feußner, 2003).

to the first part which is very flexible and allows the user to control the dialogue, the procedural part follows an exact surgery schedule which has been modelled in the Spoken Dialogue Ontology.


As these instruments and materials, which are necessary to perform each procedural task, are clearly defined, it is possible to predict the surgeon's utterances during each step.

the speech interface, the dialogue and the communication style were assessed positively which leads us to the conclusion that we can confirm our claim that the surgical environment presents a field of application for Spoken Dialogue Systems.


Afterwards, the surgeon starts with the operation and IDACO escorts the team throughout the entire surgery.

the database contains a list of all existing devices as well as the corresponding device parameters.


• Guideline for Response(Declaration) The tag "Response(Declaration)" should be applied to a speaker's response presented by declarative sentences.

to identify the dialog act of a user's utterance, a Support Vector Machine (SVM) (Cortes and Vapnik, 1995) was trained with features such as the character n-grams, word n-grams, and semantic categories.


The probability given by LIBLINEAR (Fan et al., 2008) is used as the reliability of the classification.


First, errors often crop up when a previous utterance is long and consists of several sentences.

the performance on the imbalanced dataset was not good.


The CASCADES is integrated with the MATTER method (Pustejovsky et al., 2017) for annotation and data modelling, conceptualized as the Model, Annotate, Train, Test, Evaluate and Revise cycle which inspired the presented methodology.

the DM, designed as a set of processes (threads), receives data, updates the information state and generates the system next action(-s), see also (Malchanau et al., 2015).


Modality corresponds to the speaker's evaluation of the probability of events; it concerns what the speaker believes to be possible, necessary or desirable.

this opens the possibility to specify quite detailed information about the semantic content of dialogue acts, including domain-specific semantics as shown in (Petukhova et al., 2017a) for negotiations.


An extended ISO 24617-2 metamodel (see concepts marked red in Fig.

the classified modality related to the speaker's preferences, priorities, needs and abilities is defined (Lapina and Petukhova, 2017).


Recordings that show an obvious influence of the researcher on the produced utterances have not been included in the corpus.

no metrics have been recorded to attest for this.


An overview of the entire corpus is shown in  Two different lexical analyses were performed in order to evaluate the appropriateness of the corpus for research in dialogic alignment of RL and conceptual pacts: Firstly, a trend of lexical convergence was observed both within speakers (i.e.

(2015): They communicate freely via speech but cannot interact in any other way.


In this endeavour, they use word vectors in combination with deep neural networks to determine the dialogue act of an utterance.

hence, our evaluation comprises two steps: 1.


Deictics: point to a location in space, for example, an object a place or a concrete direction), 4.

we build five datasets of non-verbal signal sequences representing the five engagement levels.


Nominal and prepositional phrases are in focus, the results are often comparable to text chunks, but the approach is closer to grammatical rules and to the linguistic understanding of a phrase.

a series of queries are performed on a SQLite database to generate web pages, in a diachronic way to see the keyword evolve in the course of time but also classified by speaker.


Data and visualization are both accessible online.

their copyright status makes them highly relevant for replication studies as well as a wide range of purposes.


testing whether hunter-gatherer languages are more likely to make a lexical distinction than agriculturalists is interesting because it provides a data point towards understanding whether there are cross-linguistic differences in languages spoken by hunter-gatherers and agriculturalists.


This and similar visualizations are unquestionably useful for doing exploratory analysis.

a map projection is an attempt to portray the surface of the earth onto a flat surface.


We hope that GermaParl makes a useful contribution to a growing family of corpora of plenary protocols.

the appropriate approach to handle this is to implement things in a fully object-oriented fashion.


The appropriate approach to handle this is to implement things in a fully object-oriented fashion.

the tool contains a backend which is used to select speeches for annotation via stratified sampling, thereby ensuring a balanced sample of speeches to be annotated, i.e.


Documents offer minimal metadata (legislative period, session number, date).

the ultimate aim of the project is to develop a generic workflow and a framework for preparing corpora of parliamentary protocols.


A problem that appears for addressee identification is when the addressee is not mentioned in the narrative.

for the purpose of specifying our method, we make the following assumptions, aimed at covering differing author styles.


An explicit mention in a line without a speech verb may be the addressee, but can also be a passer-by or a person not currently present.

typically, the goal has been to mirror relations between entities or events extracted from the text as a whole.


Table 3 shows the variation of indicators for speakers across the authors.

our results on speaker identification are relatively similar to previously obtained results (Elson et al., 2010;He et al., 2013;Muzny et al., 2017).


Analogical reasoning dataset is compromised of analogous word pairs, i.e., pairs of tuples of word relations that follow a common syntactic relation.

we denote the headwords obtained by this method as NP-heads during the evaluation section.


We collected data from social networks, and for each word, we used the algorithm based on Soundex described in Section 4.2.1. to get all the words sharing the same phonetic codes.

a cleaning process is done on each entry: removing the words (bold examples in Table 6) that are not related to the entry, removing the words (in italic in Table 6) which are similar, but they do not have the exact meaning as the entry and adding the missed words (examples in blue in Table 7).


Comparable corpora of different levels of comparability (Sharoff et al., 2013) can be used for induction of bilingual lexicons from small seed dictionaries.

one test set was based on the European Commission reports, another one on news wires concerning Donald Trump.


Similarly, in the "Examples" tab, user can click on a "Show" button and the examples from PCEDT 2.0 are shown in the tred 11 viewer/editor, which must be installed locally.

mappings between CzEngClass entries and the entries in the other lexical resources are not necessarily 1:1.


These additional pieces of information are not used in the combination approach.

of the combination, we will first of all have, more translations for lemmas in the four languages, and new target languages that haven't existed before in the original dictionaries.


more words are introduced at the lower levels (A1 and A2), but this trend reverses at the intermediate levels (B1 and B2).

in a second step, the RFL are weighted by a dispersion index (D), intended to counteract the effect of context-specific low frequency words being overused within a small number of texts.


Similarly, the availability of frequencies per level allows to rank words assigned to the same level of proficiency by frequency.

compared to invariable words (such as adverbs, prepositions, conjunctions), they would seem less frequent in texts than they really are.


This observation is of particular relevance when estimating counts from textbooks.

such descriptions remain elusive and the limitations of the CEFR for practical purposes have been stressed (North, 2005, 40).


To provide a resource contribution, we have therefore collected and released a small, high-quality en-eu corpus called Berriak (news in Basque)   Table 3: BLEU score of the models over the WMT16 IT corpus.

we can observe that the numbers are clearly higher when Basque is the target language, which matches our intuition that Basque is more difficult to translate into.


We can see that for sentence 2 the OpenNMT model has provided a translation identical to the ground truth, probably thanks to the fact that there are sentences with the same structure in the training corpus.

experiments have been conducted in both english-to-Basque (en→eu) and Basque-to-english (eu→en) to assess performance in both directions.


The NMT and SMT models have outperformed Google Translate when using training and test data from the same corpus (PaCo2 EnEu).

for English, we have used the available CommonCrawl pre-trained embeddings 3 .


In our work, we have used it from the convenient Google Cloud Translation API 2 .

the training corpus of Google Translate is certainly much bigger, and that has helped it achieve better results on Berriak.


The expressions classify the results as correct, incorrect or requiring a manual check.

the database is constantly growing and the more reports are carried out, the less manual work of inspecting segments that are neither covered by a regular expression nor by a positive/negative token is needed.


Our experimental results show that this approach can alleviate the under-translation problem, especially can sharply reduce the number of under-translation cases for the words that should be reordered during translation.

we exploit the pre-ordering for NMT to alleviate this problem.


Baseline: japan freezes its offer of humanitarian assistance to russia in the interim .

during training when decoding, there is a possibility that we feed our model with an estimateŷ t−1 coming from the model itself rather than true previous token y t−1 .


There has been some work in the field of NMT.

in common practice, the decoder uses gold reference as history during training, but it has to use generated output as history during testing.


Wiseman and Rush (2016) take a similar approach and regard training as beam search optimization.

the phrase we mention here has the same meaning as the one in phrase-based machine translation, which denotes any consecutive word sequence.


Neural machine translation (NMT) becomes a new state of the art and achieves promising translation performance using a simple encoder-decoder neural network.

calculating Levenshtein distance between the testing sentence and each sentence in the filtered set is still not fast enough.


In order to generalize the process of identifying idiom occurrences, we lemmatize the phrases and consider different re-ordering of the words in the phrase as an acceptable match.

in the next step we sample without replacement from these sets and select individual sentence pairs to build the test set.


We observe that for some idioms the literal translation in the target language is close to the actual meaning, while for others it is not the case.

we automatically select sentence pairs from the training corpora where the source sentence contains an idiom phrase to build the new test set.


The English terms distortive effect and distorting effect, both belonging to the IATE-814939 entry, are similarly frequent in English as well as their translations into Slovene.

the French translations of the term electrical engineering, i.e., electrotechnique and génieélectrique, are both frequently mentioned in the used corpora.


In this extremely low-resource task, we found that a phrase-based MT system performs much better than other methods, including a g2p system and a neural MT system.

this is not too surprising, especially since most languages use Roman script.


For each query-generated output pair, we asked participants following questions : • Is the question grammatically correct?

search engines are evolving to save time for users and increase their productivity.


Moreover, perplexity makes little use of infrequent words; thus, it is not appropriate for evaluating distributed presentations that try to represent them.

distributed word representation is known to improve the performance of many NLP applications such as machine translation (Chen and Guo, 2015) and sentiment analysis (Tai et al., 2015) to name a few.


It provides human ratings for the similarity of 3,500 verb pairs so that it enables robust evaluation of distributed representation for verbs.

a word similarity task and/or a word analogy task are generally used to evaluate distributed word representations in the NLP literature.


The smaller classes gave rise to the somewhat inflated % IAA score for Croatian because of the larger number of true negatives (verbs that are correctly found not to go in the same class).

what is more, some display a degree of semantic vacuity, that is, have little semantic content of their own and tend to express a more precise meaning when combined with some other word (e.g.


The result for the first baseline, 0.0, is the same as in SemEval, and a natural consequence of B-Cubed since there are no pairs within a class.

in order to measure the overlap between classifications produced by annotators for each language individually and across languages, we calculate percentage inter-annotator agreement (% iAA) for all pairings of verbs.


In our work, we not only indicate senses with the method which is based on similar knowledge (Levy and Goldberg, 2014), but also improve the associate distributional word representations.

we compute the context vector of each instance where C(w i ) is the context set of word instance w i which contains prototype of words, and v g (t) is the global vector of word t. we have where v c (w i , k) is the context vector of the k th sense of word w (prototype of w i ).


The best performing are 200-dimensional embeddings trained with a 5-word context window, achieving a Spearman correlation of 0.524.

the introduction of computationally efficient neural network-based methods for unsupervised learning of word embeddings from large unannotated corpora has been a watershed moment in natural language processing in recent years.


This is not an uncommon phenomenon among lowresource languages, since creation of such resources requires significant time and manpower.

since our focus is on learning representations of Urdu words only, we remove all non-Arabic script characters from the input.


While the language modelling neural network of Bengio et al.

without sufficient labelled data, it is very difficult to build natural language processing systems that can learn useful patterns which generalize well.


These approaches enable word representations to properly capture the distributional hypothesis by measuring the commonality of the linguistic contexts of word occurrences.

this may be due to the fact that the tags attached to an image are tightly associated with the image, whereas linguistic contexts, or context windows, are more generous to include weakly associated words.


Mainstream approaches employ machine learning techniques to integrate/combine visual features with linguistic features.

this confirms that the proposed method could be effective in excluding antonyms from the other semantically similar/related words.


CyTag produced 13,220 words (excluding punctuations) of which 3,716 (28.11%) are function words; WNLT produced 14,435 words (excluding punctuations) of which 5,314 words (36.81%) are function words.

as mentioned earlier, initially developed for English, it has been extended and modified to cover an increasing number of languages.


The rising prominence of corpus-driven approaches in linguistic studies has sparked a growing interest in applying a corpus-based approach to metaphor analysis in the political domain (Charteris-Black, 2004Ahrens, 2009;Deignan, 1999Deignan, , 2005Semino, 2006Semino, , 2008Deignan and Semino, 2010).

the Strict Father model and the Nurturant Patent model) and found support for Lakoff's (1996Lakoff's ( , 2002 hypothesis that Democrats and Republicans view the world differently.


Researchers in many fields are able to use the multiple search functions on the website for their specific research purposes.

after two linguisticallytrained native speakers of Chinese manually checked the corpus, the problematic taggings were revised and the tagged Chinese corpora provide a reliable Chinese database with a wide range of syntactically tagged texts.


The best results in this research occurred at the 7 th iteration for K = 3 and distance weights, where we obtained an accuracy rate of 59.8%.

these labels can be obtained in an unsupervised manner, that is, it does not make use of sense-tagged data, and this method is knowledge-based because the WLSP is a thesaurus.


Several words can have the same precise article number, even when the semantic breaks are considered.

the condition variations that we now considered were as follows.


We again obtained the best results when using only concept embeddings.

we investigate the optimal number of iterations experimentally in Section 4.


If there was a sentence including the word below, the surrounding word vector for the word is the concatenated vector of the word embeddings or the words , , , and .

the surrounding word vectors of A are created not from all word tokens of A, but from only word tokens of A predicted as Sense 1 at the n-1 prediction.


In this paper, we consider several implementations of the feature space to which x belongs.

this architecture must be trained in a layer-wise fashion (train layer , disambiguate training data, train layer + 1, etc.


The universe of WSD approaches is usually divided into the two main categories of "supervised" and "knowledge-based" methods (Raganato et al., 2017).

in the IMS case, semantic features lead to more evident benefits w.r.t.


The tooltip shows its lemma "experiment", the synset identifier (36055), and the words forming the synset "experiment", "experimenting" as well as its hypernyms "attempt", "reproduction", "research", "method".

2 https://github.com/nlpub/word2vec-pyro4  Figure 1 shows the Web interface of Watasense.


Our accuracy was lower than that of the recent Korean WSD study, which employed a supervised approach (Shin and Ock, 2016); however, our method is unsupervised, and therefore has the advantage that it can be applied to any document without learning.

it is expensive and time-consuming to construct such corpora.


The merging phase identifies identical sentences with annotations on different words, and creates a single sentence containing all annotations.

original annotations are done with WordNet 1.6.


In fact, this happens to a large extent on adjectives, causing the largest losses.

the latter's emphasis is to model opposite meanings (antonym-like) as highly non-similar, e.g.


As an example, consider processing the ambiguous word bank as depicted in Figure 2: when observing an occurrence of this word in a sentence about a financial topic, a classifier like fastText will likely suggest topic labels such as finance, money or financial institute because of the fact that the input sentence is about such a topic.

we also tested fastSense on SemEval-2007 Task 17 Subtask 1 (SE7) and Subtask 3 (SE7'), SemEval-2013 Task 12 (SE13) and SemEval-2015 Task 13 (SE15).


The paper is structured as follows: In Section 2., we contrast fastSense with related approaches to WSD.

fastText is not suitable for disambiguating words.


In Section 4., we explain the experiments carried out to eval-uate fastSense and show the results achieved by it.

we do not necessarily select the label of highest probability, but go through the list of rankordered candidates until the first occurrence of x tagged by a corresponding sense number is reached.


TAG also utilizes a range of visual encodings to identify relationships and types, and includes an alternative representation of linguistic relationships.

this interest has been properly disclosed to the University of Arizona Institutional Review Committee and is managed in accordance with its conflict of interest policies.


Purgina and Mozgovoy 2017introduce WordBricks, which also utilizes a container layout to identify linguistic structure while maintaining an explicit sequential representation that is easy to read.

bRAT does not support the ability to draw links between links, which makes it difficult to represent relations linking several predicate-less relations, a feature necessary to completely describe complex events.


For this reason, in computer science automatic image annotation has been studied for some time now (Jeon, Lavrenko and Manmatha, 2003;Li and Wang, 2008).

its collaborative annotation framework follows a client/server architecture, which facilitates the work of multiple users on consistent data sources.


We also release analogical clusters and analogical grids produced from a parallel corpus on 11 European languages using this Python module.

all of these procedures are performed seamlessly to the users by the Words2Grids program.


These examples of role language were also found in translated novels 8 but were used less than in Japanese novels.

these cases cannot be categorized into detailed age groups.


It supports various types of text format including plain text and CoNLL format.

span is represented as a page number, positions, and its label, while relation contains a page number, connection type, two identified spans and its label.


following this track, we have laid the foundations for an annotation language that is compatible with Legal-RuleML (Nazarenko et al., 2016).


We first observe that there are inconsistencies at the token level since not all instances of an event are always annotated as such.

it uses a variety of sources for its data, including WSJ news.


Figure 3 summarizes the document intersections of these three corpora and related ones.

many multi-token events in FB are composed of numbers in combination with symbols (#), currencies ($, us$, c$ ) and percentages (%).


Space correction based on learning To correct the spaces within multi-token words based on learning methods, a model was trained to find words with multiple parts separated by spaces.

for this reason, we collected a list of verb roots in past tense and present tense forms.


Moreover, even using a language-specific tokenizer might not always solve this problem, because some tokenization ambiguities cannot be solved other than by an arbitrary choice depending on interpretation; for instance, the documentation about the way tokenization is carried out for French in the UD2 corpus mentions that: "This tokenizing and segmentating choice is arbitrary and other French treebanks could choose to do otherwise."

many NLP shared tasks nowadays provide datasets annotated with the linguistic information relevant to the task, so that participants can focus on the core aspects of the task rather than spend time on non-essential pre-processing steps.


The mentioned cases are just few examples to show how complex the Arabic lemmatization is, and reveal that many cases should be considered in addition to stripping words from prefixes and suffixes to get their proper lemmatization.

for example, stem patterns for broken plurals are different from their singular patterns, e.g.


While BLEU is designed to benefit from multiple reference translations (Papineni et al., 2002), this is a luxury rarely afforded to the modern system, and both of the major MT workshops use single-reference BLEU to evaluate all their tasks 34 .

many of our translations are crowdsourced, and there are no guarantees on the fluency of the participants.


and affective (e.g., negative and positive emotions) properties.

the b5-post subcorpus -containing Facebook status updates -is a textual base developed primarily for the purpose of personality recognition and author profiling in Portuguese.


Other possible applications include the use of the annotated data for classification algorithms (e.g., to predict a topic category given a letter) or a part-of-speech tagger for historical text.

note that the measurements include an automatic modernization layer for lemmas, implemented using look-up on an authoritative source (de Vries and te Winkel, 1998).


Variation is influenced by linguistic contexts as well as sociolinguistic factors (e.g., intended audience).

as administrator of the region, Hooft was responsible for the implementation of this law.


Therefore, the percentages mentioned above are approximate and the observations need to be confirmed in future research.

intra-speaker variation in speech corpora (Schilling-Estes, 2002;Szmrecsanyi, 2005) or intra-author variation in English literature (Leech, 1969;Busse, 2002).


Note that in Algorithm 1 the first direction is implemented as a special case of the second direction (i.e., skipping 0 tokens).

this preliminary quantitative analysis is consistent with the idiom hypothesis, although other hypotheses might explain the observations equally well.


The task of constructing the corpus and selecting the core vocabulary was performed according to the following procedures: 1.

1 It has many grammatical mistakes and you cannot understand the meaning.


In [0.7, 0.8], there was a tendency to simplify only one word.

according to the standard of the Japanese Language Proficiency Test (called JLPT) Level 3 (level of understanding elementary Japanese), it is necessary to master 1,500 words.


Nevertheless, Classical Chinese is largely neglected, mainly owing to its obsoleteness.


Hence, we have attributes like prompt adherence here, rather than word choice.

one common feature that all the above work has in common is the fact that the essay grading dataset that they used was the ASAP AEG dataset.


Each prompt was split into sets of 100 essays each, with the assumption that a set would correspond to a week's worth of time for the annotator.

this is mainly because source-dependent essays are highly dependent on the source text, while argumentative / persuasive essays can utilize arguments from beyond the scope of any text, and so, those arguments have to be coherent and cohesive.


The corpus contains texts that could be collected in electronic format only, involving no OCR of scanned paper printed books.

), • diacritics insertion in the texts that lacked them or diacritics replacement (when non-standard ones occurred).


BioRo corpus contains, excluding the punctuation, 9,864,707 tokens distributed in different medical subdomains such as: diabetes, endocrinology, cardiology, oncology, neurology etc.

the POS tagging accuracy for the general purpose Romanian language is over 98% (tufis, 1999), and for the biomedical domain the accuracy is 97.83% (Mitrofan and Ion, 2017).


The Romanian language is an under-resourced language, regarding resources available for bioNLP.

we presented the BioRo corpus which contains morphosyntactically annotation.


Research effort in the recognition of affect in text has focussed to a large extent on recognition of positive/negative sentiment, while more nuanced emotion representation models have received relatively little attention.

beyond simple positive/negative sentiment, there are two main emotion representation schemes that have been used in automated emotion recognition research.


Code-mixing provides some challenges for language identification like spelling variations and non-grammatical structures in a sentence (Barman et al., 2014).

last line determines the category in which the tweet belongs i.e.


In this study, we constructed a dialogue corpus for persuasive dialogue systems via crowdsourcing, including the labeling of emotional states and the acceptance of users' utterances.

if there are small mismatches between the desire and the belief, the model evokes a positive emotion such as "happy" or "contentment".


This makes the generation of future updates straightforward upon improving the quality of the resources.

each lemma-POS pair is associated with three sentiment scores (positive, negative and objective), in addition to the english gloss.


When evaluated on the PATB test set, SentiArabic achieves an accuracy of 76.7% and a weighted F-score of 76.5%.

we find the increase insignificant given the computational overhead incurred by MADAMIRA, which is in favor of the lightweight SentiArabic analyzer.


as shown in this paper, there is a strong correlation between the amount of context and the performance of an emotion recognition system despite of the amount of data i.e.


The delay between an actual change of emotional behaviour and the moment it is annotated is called reaction lag (RL).

this methodology may lead to a loss of information contained in annotator's perception of emotions.


Your task is to identify the emotions that the art evokes, that is, all emotions that the art brings to mind.

the WikiArt Emotions Dataset is made freely available for educational purposes and to facilitate research in emotions, art, human psychology, and automatic image analysis/generation.


Many extra emotions were entered by the annotators, including uncertainty, amusement, and jealousy.

these annotations will help test the hypothesis that art that depicts the left side of a person's face or body is on average found to be more appealing (left-cheek bias) (Powell and Schirillo, 2011;Blackburn and Schirillo, 2012).


(Fleiss' κ calculates the extent to which the observed agreement exceeds the one that would be expected by chance (Fleiss, 1971).

we chose a somewhat generous aggregation criteria: if at least 40% of the responses (four out of ten people) indicate that a certain emotion applies, then that label is chosen.


• To conduct experiments to determine what characteristics of images make them particularly evocative.

a number of museums now have a substantial online presence.


we only compare our results with systems, which reported their results on EK6.


Such substitutes can easily be extracted from electronically published educational medical textbooks.

the problems we address here are often deeply rooted in national legal systems world-wide and reflect fundamental economic as well as social concerns (Mittelstadt and Floridi, 2016).


The context captured in the features is very limited and usually includes only the current word and a preceding and following word.

this must be verified on a case-by-case basis and it is achieved when an author is able to put their personal stamp onto the work through free and creative choices.


Consequently, it should not come as a surprise that the right of translation was the first right to be included in the minimum standard of protection in the oldest copyright international treaty, the Berne Convention.

5(1), which covers acts of temporary reproduction, only exempts the activities listed in points 1) to 4) above, whereas the activity of point 5), i.e.


Most of the texts that can be found online do not carry any licence at all or are part of commercial offers which do not permit reproduction.

the real test is that of the author's own intellectual creation.


Figure 2 shows a sample of feature values generated by the Stanford NER.

the SA clause that applies in the present scenario requires that distribution of the adapted material be made under the terms of the same licence or a later version with the same terms.


set of texts) that is similar to the corpus to which it is later applied; i.e., it must be of the same language and domain or text type and annotated with the appropriate labels, e.g., "English", "Social Sciences", "scholarly publications" and "named entities" (NE), respectively.

it is important to determine whether the trained model is an adaptation of the original annotated corpora.


shows a portion of the LAPPS Grid Galaxy interface and a workflow in which a tokenizer and part-ofspeech tagger from WebLicht are invoked, followed by a named entity recognizer from the LAPPS Grid.

conversion from LIF to TCF requires mapping character offset anchors to each token element, and conversion in the reverse direction demands recomputing offsets from the primary source.


The scheme of Fluid Annotation comprised of three main components: DeepLexicon, Fluid Segmentation & Tagger, and Annotation UI ( Figure 1).

different segmenters differs on the particular algorithms they implemented.


While Hungarian can be considered an under-resourced language it does have an active and cooperating language technology community which has been developing various tools to cover the basic text processing steps.

it takes the text and the list of analytical modules that the user wants to run as input and provides GATE generated XML containing the annotations as output.


Word embedding models can be used to satisfy recurrent tasks in NLP such as lexical and semantic generalisation in machine learning tasks, finding similar or related words and computing semantic relatedness of terms.

jOBIMTEXT is a semantic similarity tool that implements its own algorithm named joBim (Biemann et al., 2013 Table 1: List of functionalities and framework coverage.


In addition to the vector file, INDRAINDEXER also generates a metadata file containing all the parameters from both the preprocessing and generation steps.

building and consuming specific word embedding models require the setting of a large set of configurations, such as corpus-dependant parameters, distance measures as well as compositional models.


To faciliate error analysis, one of the authors then coded each homograph as either morphosyntactic, lexical, or mixed; the counts of these three categories are given in Table 1.

rule-based components represent substantial barriers for both quality control and internationalization.


Since the output of a normalizer is the input of the next normalizer, each bar is as long as the non-rejection part of the bar above it.

while the individual actions performed on the string are expressed as Thrax grammars, the string variation normalizer allows us to specify them without context.


(2016), Zhang and Lapata (2017) reformulate this approach in the form of neural encoder-decoder models.

we then chose at most 15 instances for a random selection of modifiers from each band for inclusion in the final dataset, which contains a total of 3632 instances: ROCStories -3026 instances.


The best overall stemmer is Ljubešić and Pandžić's stemmer for Croatian, although the optimal stemmer of Kešelj and Šipka is a close second.

the proposed negation-marking technique is found beneficial on the word2vec baseline.


The effects of various morphological normalization techniques on STS model performances have also been evaluated.

this effect was also evident when applying POSt StSS to data in English (Batanović and Bojić, 2015), and was previously noted by other researchers as well (Wiemer-Hastings, 2004 table 6: StS model performances on 10-fold CV content and are therefore assigned a lower weight, most often in the 0.7 -0.9 range.


For the sake of standardization, we chose to follow the annotation methodology established in the SemEval STS tasks, and we adopted the scoring scheme (a 0 -5 Likert scale) and the general annotation guidelines used therein .

lInSTSS generally achieves results similar to POST STSS, but the POS-TF STSS mixture model performs better than both lInSTSS and POST STSS independently.


The optimal string similarity weight in the basic Islam and Inkpen approach tends to be 0.7, resulting in an optimal semantic similarity weight of 0.3.

most of this development has been limited to English.


When annotating atomic phenomena, our experts identified and annotated the type, the scope, and in some paraphrase types, the key element.

when the annotation process involves more than two annotators, we first calculate the pairwise TPO measure between any two annotators and then we use one of three different techniques for calculating the overall agreement for the corpus.


The meanings of "beat" and "battled" are similar, and play the same syntactic and discourse role in the structure of the texts.

the annotation process included three expert linguists and covered the whole 5801 text pairs from the MRPC corpus.


People make assertions on a controversy (or controversial issue) both in the physical world and on social media.

participants were presented with four assertions at a time and asked two questions: 1. which of the assertions they support the most (or oppose the least), 2. which of the assertions they oppose the most (or support the least).


To calculate these scores, we reuse the formula shown in equation 2.

these approaches focus on identifying sentiment, stance, claims, premises, reasons, arguments, sentiment, etc.


From the agreement judgments, we created the agreement matrix AM , which contains one column per assertion, and one row per participant.


The second goal of this work is to summarize information about controversial issues without necessarily being dependent on the described crowdsourcing.

the crowdsourced data will serve as a source of reference (gold) labels for the evaluation of these NLP algorithms.


Since it is difficult to give a numerical score indicating the degree of support or degree of opposition, we will give you four assertions at a time, and ask you to indicate to us: -Which of the assertions do you support the most (or oppose the least)?

individual participants were free to judge as many assertions as they wished.


during collection, the data was not sampled on the basis of language and so it included data from English, Hindi as well as some other Indian languages.


While most of these behaviour like bullying or hate speech have predated the Internet, the reach and extent of the Internet has given these an unprecedented power and influence to affect the lives of billions of people.

there is hardly any theoretical insight into the structure and formation of such behaviours (some notable exceptions include Hardaker, 2010Hardaker, , 2013, in general, and absolutely nothing in Indian scenario.


The verb extend (in the sense "extend an offer") seems not to fit the pattern; without the Entity Offered/PAT being a noun phrase meaning exactly what the class is about, namely offers, bids, aid, etc., its meaning is more similar to "hand over" than to "offer".

500 classes), we will compare the results with automated synonym discovery methods, such as (van der Plas et al., 2011;van der Plas et al., 2014), either using Deep Learning (looking, e.g., at embeddings based on argument-role mapping) or other previously well-researched methods, such as the LDA which has been already used for Czech, e.g., in (Materna, 2012).


It is a manually annotated Czech-English valency lexicon linking verbal entries of PDT-Vallex and EngVallex.

from the example below, it is clear that the translation was quite adequate and thus make available should be kept in the synonym class OffER (see the deep dependency annotation of this example captured in figure 2) where the Recipient is annotated with the relation BEN as a dependent on available, denoted as Eff(BEN) in this is considered pure nominalization (other sentences have been found where offer, bid, etc.


We will measure this by looking at a target word's relation set, for instance its synonyms, and top n-most-similar words based on the embeddings model.

for the general domain, there exists a wide range of gold standard resources for evaluating distributional semantic models in their ability to capture semantic relations of different types, for instance, Simlex-999 (Hill et al., 2015).


We explored three alternative levels of morphological term representations, surface form, lemma, and multiple lemmas, all complemented by the clustering of morphological variants.

in addition, we performed an error analysis and found that 62% of the related terms groups were indeed irrelevant.


In addition, we performed an error analysis and found that 62% of the related terms groups were indeed irrelevant.

since these terms do not appear in Hebrew WordNet, they were counted as false positives and decreased the overall MAP.


The BHC will be both lemmatized and POS-tagged by using TreeTagger (Schmid 1994(Schmid , 1995 or Frog (Avontuur et al.

an investigation in the 1980s into phonological change in West Frisian and Dutch of (the same) West Frisian speakers, suggests that, at least at the phonological level, the opposite holds: younger speakers were more likely to keep the phonological rules of the two languages apart; whereas older speakers were more likely to confuse them in either language (van der Kuip 1986, Feitsma et al.


The gaps between these anchor points are then filled using BLEU-based and lengthbased heuristics.

secondly, we use English-to-Portuguese model to translate the machine translated English sentences into Chinese output.


However, Chinese and Portuguese belong to distinct language families (Sino-Tibetan and Romance, respectively) and only a relative much smaller proportion of people have bilingual proficiency of the language pair.

for instance, we could train a SMT system on the sub-corpus in newswire domain and use the system to translate sentences for translation based alignment method.


As shown in Figure 1, there is usually a language switch button on the top of a web page which can be used to extract its corresponding page in the other language.

as shown in Table 5, the pivot-based systems perform poor than those trained on direct parallel corpora.


Each image comparison HIT showed a single sentence with the possible images below it.

(2010) for the PASCAL Data Set (Everingham et al., 2011), which consists of 5000 descriptive sentences, 5 cap- • The large prawn is on top of the stool.


The oracle system evaluation is reported in table 3.

the framework is evaluated using French broadcast news drawn from the REPERE corpus.


The second one consists of asking a human to correct the output of the first part.

there are two possible strategies to measure the action duration: 1. using the average of time of annotators having the same experience; 2. using the average of time of annotators having several experiences.


Figure 2 clearly shows the difference between the behavior of the TE label prediction model trained on the SNLI corpus and the model trained on the SICK corpus.

hypothesis sentences of the SNLI corpus have a hidden bias to allow prediction of their TE labels without premise sentences.


In fact, results are mostly lower for the Croatian than for the English, with the exception of one case: fastText-Skip for WordSim-353.

the difference in English (Germanic language) and Croatian (Slavic language) morphology is huge.


Excluding relations of place and time (which may apply to static or dynamic scenes), these fall under the CONFIG-URATION portion of the supersense hierarchy.

in this paper, we show that a tagset for broad-coverage semantic annotation of prepositions and postpositions can be applied-mostly as is-to English possessive constructions.


In the LCC dataset, the word pair structure of tariffs is scored as a "3" (high metaphoricity), and this is a reasonable metaphoricity score; clearly, a tariff cannot have a physical structure.

in this work, we describe a new, large dataset (18,000+ instances) annotated for metaphor novelty, built on top Baseline Description RANDOM Predicts a random continuous value in the range of 0-3 for each instance.


The first experiment aims to estimate the fraction of spurious sense clusters, the second one evaluates the quality of the post-processed hypernyms.

of related ambiguous terms (Biemann and Riedl, 2013); 2word sense induction via clustering of ego networks (Widdows and Dorow, 2002;Everett and Borgatti, 2005) of related words using the Chinese Whispers graph clustering algorithm (Biemann, 2006); (3) disambiguation of related words and hypernyms.


Swampillai and Stevenson (2010) identify nearly 28.6% of the relations appearing across sentences in the MUC6 dataset.

given the above three strands of research related to the field of relation extraction, it can be clearly noticed that significantly a large number of studies have focused on intrasentence relation extraction in comparison to the research work on inter-sentence relation extraction.


Our dataset is created following the distant supervision approach.

previous studies on inter-sentence relation extraction have employed smaller datasets (Swampillai and Stevenson, 2010;D'Souza and Ng, 2014;Gu et al., 2017).


However, the study is focused on a specialised domain such drug-gene interaction.

further, let us also assume that e 1 is included in a sentence s i and e 2 is included in a sentence s j .


There were also other relations that had lesser than 1500 sentence pairs, which are not listed in Table 2, as we do not include those relations in the dataset.

the performance obtained using BIL-STM does not provide a statistically significant improvement against the other examined models.


Nevertheless, as we discuss in Sections 2 and 3, diacritics restoration (also known as diacritics generation or diacritization) is an active problem also in many languages with substantially lower diacritics appearance.

in recent years, deep neural networks have shown remarkable results in many areas.


From the directed graph A, a Minimum Spanning Tree (MST) is constructed, using Chu-Liu-Edmonds algorithm (Chu & Liu, 1965;Edmonds, 1967) where the weight of each edge is 1/|V|.

at the opposite pole, the literature domain, even if it does not contain long sentences, raises the most parsing problems: the authors' creativity manifests both in the sentence structure (dislocated arguments, unlexicalised ones, unusual word order, etc.)


Ellipsis is an important challenge for natural language processing systems, and addressing that challenge requires large collections of relevant data.

table 2 gives the distribution of these data points by the    (Fernández et al.


The majority baseline results are shown in Table  8.

it has a very low recall when identifying None type sluices.


We establish several strong baselines for the emotion detection task on dialogues, and motivate an automatic metric to benchmark progress.

recently, emotion recognition researches on text focus on the learning-based methods.


The EmotionPush chat logs are from private conversations.

we adopted Fleiss' kappa to measure the agreement among annotators of the labeling task of the dataset.


Those utterances with more than two different emotions voted were put into the non-neutral category.

within a dialogue, there is a high probability of inter-dependency with respect to their sentimental clues.


The IT environments typically used in research institutions (operating systems, programming languages, software libraries, database systems, etc.)

3 this also has unwanted side-effects; First, it means that many posts are annotated only according to one particular category, i.e., the data sets for the categories are mostly disjoint and consequently separate classification models must be trained, rather than a single multi-label model.


We gave the developers a month to find contributors and collect judgments, and we paid them awards for successfully building applications and for the quantity and quality of judgments that they submitted.

we chose to collect judgments on the toxicity of Wikipedia discussion comments, where a "toxic" comment is defined as any kind of hateful, aggressive, or disrespectful comment that is likely to make someone leave a discussion.


These new configurations outperform both the baseline systems (jv and  su) and their performance is tied with the Indonesian system (the differences with Indonesian are not statistically significant).

similarly, we used the Javanese and sundanese multi-speaker corpora individually to construct single-language (but multispeaker) systems jv and su, which serve as baselines in our experiments.


Thus, useful feedback consists of clear reasoning and objective evidence supporting factual claims (Park et al., 2015).

we restrict the notion of verifiability to pieces of evidence that may be available at the time the claim is made; predictions about future are considered unverifiable.


Having many specific premises, a subset of which may not be present in the text, makes it difficult for manual annotation and automatic classification.

many comments are not written this way, thwarting the government agencies' effort to communicate with citizens.


Subject-matter RST Relations and Argument Schemes.

"Admittedly they do not generally prevent you from catching a virus").


• Other clues: the premise contains adjectives or other linguistic items which qualify something as more or less good • Inferential rules: if something is of important value, it should not be terminated if something has a positive value, it should be supported/continued/promoted/maintained if something has positive effects, it should be supported/continued/promoted/maintained if something has a negative effect it should be terminated These two argument schemes are perceived as similar due to the common reference to intentionality and to the frame of human action.

a complete list of the inferential rules is available in the annotation guidelines.


Generally, the automatic search for arguments encompasses the following steps (Peldszus and Stede, 2013): 1. the segmentation of texts into argumentative discourse units(ADUs); 2. the classification of the role (e.g., claim, premise) played by each ADU; 3. the analysis of the relations linking ADUs (e.g., support, attack); and 4. the identification argument schemes, namely the implicit and explicit inferential relations within and across ADUs.

they have been conceived as arguments which support the negation of the proposition functioning as claim.


We used the parallel corpus developed by Farhath et al.

hence checking the casecount combinations of a word when substituting, helps to preserve language semantics of the generated sentence.


That is, each word begins with a 'd' sound and it contains a consonant cluster 'nt'.

some data points in BDPROTO represent root-level language family nodes, such as Indo-European.


Even if we followed a linguistic approach there were 16,473 unique lemmas in the training dataset.

we use string-wise micro averaged precision, recall and F-Score to evaluate our model as is the standard with evaluating word segmentation models.


Everyday language is built up of prefabricated parts and templates that form a speaker's individual discourse experience (Hopper, 1998;MacWhinney, 2001;Bybee and McClelland, 2005).

the Constructicon lists constructions, such as the Way_manner construction (e.g., She whistled her way down the lane), and lists the roles associated with the construction and the Construction Evoking Element (CEE) (e.g., one's way in this context).


Furthermore, a goal of AMR is to provide consistent semantic representation despite language-specific syntactic idiosyncracies.

using keyword searches over the annotations and text (e.g., 'Compared-to'), we discovered an initial set of about 4,600 annotations that potentially needed retrofitting for Have-Degree/Quant-91 (the remaining cases were found to be simple usages of degree or quantity modifiers, such as very, which remain unchanged), and about 30 potential Correlate-91 cases.


Nonetheless, the selection of the appropriate root is necessarily somewhat subjective and remains a source of disagreement throughout other annotations as well.

representing the meanings associated with fully syntactic patterns required a novel annotation approach.


Moreover, such manual resources need extra effort to be maintained and updated to integrate new senses and words appearing in everyday language.

we chose wikipedia in the language L as raw corpus C L and BabelNet as the underlying semantic graph G because both are available for all the 6 languages of interest.


The toolkit comes with scripts to download and preprocess datasets, and an easy interface to evaluate sentence encoders.

over the years, something of a consensus has been established, mostly based on the evaluations in seminal papers such as SkipThought (Kiros et al., 2015), concerning what evaluations to use.


We use the average of Pearson correlations for STS'12 to STS'16 which are composed of several subtasks.

it has been argued that the focus should be on downstream tasks where these representations would actually be applied (Ettinger et al., 2016;Nayak et al., 2016).


An alternative approach to using word embeddings to extend an existing WordNet has been described by Al tarouti and Kalita (2016), who in fact use word embeddings to extend an automatically-constructed Arabic WordNet built using the machine translation / bilingual dictionary method described by Lam et al.

an extension of this work specifically aimed at lesser-resourced languages was also described, in which a Persian WordNet is constructed by finding the English translations of Persian words in small corpora using a bilingual dictionary.


Murder, violence, flashback, and romantic are the most frequent four tags in the corpus that are assigned to 5,732; 4,426; 2,937 and 2,906 movies respectively.

folksonomy (Vander Wal, 2005), also known as collaborative tagging or social tagging, is a popular way to gather community feedback about online items in the form of tags.


"), the subject of "没"(not-exist, i.e., "vanish") is omitted, which causes the problem that most parser will treat "这样"(this way, i.e., "this way") as the subject.

the annotation file is of the same format with the brat's default format.


The concept is overlapped with the concept of ellipsis, more specifically contained in the concept of ellipsis.

most of the time, we don't know what the exact wording of the omission is, but we are aware that it must be a noun and represent a thing.


The performance of ecp improves with decreasing time interval.

to establish a benchmark on detecting changes in online conversation, we collected a dataset of 16 sport events, with reference change points for each event.


Event detection detects either emerging events or specific events from raw stream data.

more analysis would be required to check, e.g.


As algorithms handle multivariate signal, we can also add counts as a fourth time series, in addition to the sentiment scores.


Coefficients range between 0.66 and 0.73 depending on the temporal tag.

also, we can infer the location of the author with greater certainty for before rather than after posting the tweet.